


import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import BridgeTowerImageProcessor

class 							a						(unittest.TestCase ):




							"""simple docstring"""


							def __init__(				self			:    Optional[Any]       ,      lowerCamelCase			:    Optional[Any]       ,      lowerCamelCase			:    bool = True       ,      lowerCamelCase			:    Dict[str, int] = None       ,      lowerCamelCase			:    int = 32       ,      lowerCamelCase			:    bool = True       ,      lowerCamelCase			:    Union[int, float] = 1 / 255       ,      lowerCamelCase			:    bool = True       ,      lowerCamelCase			:    bool = True       ,      lowerCamelCase			:    Optional[Union[float, List[float]]] = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,      lowerCamelCase			:    Optional[Union[float, List[float]]] = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,      lowerCamelCase			:    bool = True       ,      lowerCamelCase			:    Union[str, Any]=7       ,      lowerCamelCase			:    int=30       ,      lowerCamelCase			:    Union[str, Any]=400       ,      lowerCamelCase			:    Union[str, Any]=3       ,      )      ->       Tuple:
														__snake_case						:     int									=  parent
														__snake_case						:     Optional[Any]									=  do_resize
														__snake_case						:     List[Any]									=  size if size is not None else {"shortest_edge": 288}
														__snake_case						:     Tuple									=  size_divisor
														__snake_case						:     List[Any]									=  do_rescale
														__snake_case						:     Optional[Any]									=  rescale_factor
														__snake_case						:     Optional[int]									=  do_normalize
														__snake_case						:     Optional[Any]									=  do_center_crop
														__snake_case						:     List[str]									=  image_mean
														__snake_case						:     Optional[int]									=  image_std
														__snake_case						:     Optional[int]									=  do_pad
														__snake_case						:     Tuple									=  batch_size
														__snake_case						:     Optional[Any]									=  num_channels
														__snake_case						:     Optional[int]									=  min_resolution
														__snake_case						:     Any									=  max_resolution

							def        __snake_case   (				self			:    List[Any]							)      ->       Any:
														return {
														    "image_mean": self.image_mean,
														    "image_std": self.image_std,
														    "do_normalize": self.do_normalize,
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "size_divisor": self.size_divisor,
														}






							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    Optional[int]=False							)      ->       str:
														if not batched:
																					__snake_case						:     Tuple									=  self.size["shortest_edge"]
																					__snake_case						:     Optional[Any]									=  image_inputs[0]
																					if isinstance(lowerCamelCase       ,      Image.Image							):
																												__snake_case					,     __snake_case						:     List[Any]									=  image.size
																					else:
																												__snake_case					,     __snake_case						:     Dict									=  image.shape[1], image.shape[2]
																					__snake_case						:     Dict									=  size / min(lowerCamelCase       ,      lowerCamelCase							)
																					if h < w:
																												__snake_case					,     __snake_case						:     Union[str, Any]									=  size, scale * w
																					else:
																												__snake_case					,     __snake_case						:     List[Any]									=  scale * h, size

																					__snake_case						:     int									=  int((1333 / 800) * size							)
																					if max(lowerCamelCase       ,      lowerCamelCase							) > max_size:
																												__snake_case						:     Union[str, Any]									=  max_size / max(lowerCamelCase       ,      lowerCamelCase							)
																												__snake_case						:     int									=  newh * scale
																												__snake_case						:     Optional[int]									=  neww * scale

																					__snake_case					,     __snake_case						:     Any									=  int(newh + 0.5							), int(neww + 0.5							)
																					__snake_case					,     __snake_case						:     int									=  (
																					    newh // self.size_divisor * self.size_divisor,
																					    neww // self.size_divisor * self.size_divisor,
																					)

														else:
																					__snake_case						:     Tuple									=  []
																					for image in image_inputs:
																												__snake_case					,     __snake_case						:     Optional[Any]									=  self.get_expected_values([image]							)
																												expected_values.append((expected_height, expected_width)							)
																					__snake_case						:     str									=  max(lowerCamelCase       ,      key=lambda lowerCamelCase							: item[0]							)[0]
																					__snake_case						:     Dict									=  max(lowerCamelCase       ,      key=lambda lowerCamelCase							: item[1]							)[1]

														return expected_height, expected_width

@require_torch
@require_vision
class 							a						(_lowerCAmelCase	,							unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Any	    =    BridgeTowerImageProcessor if is_vision_available() else None

							def        __snake_case   (				self			:    Dict							)      ->       int:
														__snake_case						:     List[Any]									=  BridgeTowerImageProcessingTester(self							)

							@property
							def        __snake_case   (				self			:    List[str]							)      ->       str:
														return self.image_processor_tester.prepare_image_processor_dict()

							def        __snake_case   (				self			:    Any							)      ->       Any:
														__snake_case						:     List[Any]									=  self.image_processing_class(**self.image_processor_dict							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "image_mean"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "image_std"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "do_normalize"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "do_resize"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "size"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "size_divisor"							)							)

							def        __snake_case   (				self			:    List[str]							)      ->       List[Any]:
														pass

							def        __snake_case   (				self			:    List[str]							)      ->       Dict:
														# Initialize image processor
														__snake_case						:     str									=  self.image_processing_class(**self.image_processor_dict							)
														# create random PIL images
														__snake_case						:     List[str]									=  prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=lowerCamelCase							)
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase       ,      Image.Image							)

														# Test not batched input
														__snake_case						:     List[Any]									=  image_processing(image_inputs[0]       ,      return_tensors="pt"							).pixel_values

														__snake_case					,     __snake_case						:     int									=  self.image_processor_tester.get_expected_values(lowerCamelCase							)
														self.assertEqual(
														    encoded_images.shape       ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,      )

														# Test batched
														__snake_case						:     Optional[int]									=  image_processing(lowerCamelCase       ,      return_tensors="pt"							).pixel_values

														__snake_case					,     __snake_case						:     Any									=  self.image_processor_tester.get_expected_values(lowerCamelCase       ,      batched=lowerCamelCase							)
														self.assertEqual(
														    encoded_images.shape       ,      (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )       ,      )

							def        __snake_case   (				self			:    List[Any]							)      ->       List[Any]:
														# Initialize image processor
														__snake_case						:     List[Any]									=  self.image_processing_class(**self.image_processor_dict							)
														# create random numpy tensors
														__snake_case						:     Any									=  prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=lowerCamelCase       ,      numpify=lowerCamelCase							)
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase       ,      np.ndarray							)

														# Test not batched input
														__snake_case						:     List[Any]									=  image_processing(image_inputs[0]       ,      return_tensors="pt"							).pixel_values

														__snake_case					,     __snake_case						:     Tuple									=  self.image_processor_tester.get_expected_values(lowerCamelCase							)
														self.assertEqual(
														    encoded_images.shape       ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,      )

														# Test batched
														__snake_case						:     Optional[Any]									=  image_processing(lowerCamelCase       ,      return_tensors="pt"							).pixel_values

														__snake_case					,     __snake_case						:     Tuple									=  self.image_processor_tester.get_expected_values(lowerCamelCase       ,      batched=lowerCamelCase							)
														self.assertEqual(
														    encoded_images.shape       ,      (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )       ,      )






							def        __snake_case   (				self			:    str							)      ->       Tuple:
														# Initialize image processor
														__snake_case						:     Union[str, Any]									=  self.image_processing_class(**self.image_processor_dict							)
														# create random PyTorch tensors
														__snake_case						:     int									=  prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=lowerCamelCase       ,      torchify=lowerCamelCase							)
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase       ,      torch.Tensor							)

														# Test not batched input
														__snake_case						:     str									=  image_processing(image_inputs[0]       ,      return_tensors="pt"							).pixel_values

														__snake_case					,     __snake_case						:     Union[str, Any]									=  self.image_processor_tester.get_expected_values(lowerCamelCase							)
														self.assertEqual(
														    encoded_images.shape       ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,      )

														# Test batched
														__snake_case						:     Union[str, Any]									=  image_processing(lowerCamelCase       ,      return_tensors="pt"							).pixel_values

														__snake_case					,     __snake_case						:     Union[str, Any]									=  self.image_processor_tester.get_expected_values(lowerCamelCase       ,      batched=lowerCamelCase							)
														self.assertEqual(
														    encoded_images.shape       ,      (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )       ,      )









"""simple docstring"""
def 							A						(				__snake_case: int  )    ->		int:

       """simple docstring"""





       if divisor % 5 == 0 or divisor % 2 == 0:
              return 0
       __magic_name__      				=    1
       __magic_name__      				=    1
       while repunit:
              __magic_name__      				=    (1_0 * repunit + 1) % divisor
              repunit_index += 1
       return repunit_index


def 							A						(				__snake_case: int = 1_0_0_0_0_0_0  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    limit - 1
       if divisor % 2 == 0:
              divisor += 1
       while least_divisible_repunit(__snake_case  ) <= limit:
              divisor += 2
       return divisor


if __name__ == "__main__":
 print(f"""{solution() = }""")